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  <div class="section" id="tensorflow-frontend">
<h1>TensorFlow 前端<a class="headerlink" href="#tensorflow-frontend" title="永久链接至标题">¶</a></h1>
<p>TensorFlow 前端有助于导入 TensorFlow 模型到 TVM</p>
<p>支持版本：</p>
<ul class="simple">
<li><p>1.12 及以下</p></li>
</ul>
<p>经过测试的模型：</p>
<ul class="simple">
<li><p>Inception (V1/V2/V3/V4)</p></li>
<li><p>Resnet (All)</p></li>
<li><p>Mobilenet (V1/V2 All)</p></li>
<li><p>Vgg (16/19)</p></li>
<li><p>BERT (Base/3-layer)</p></li>
</ul>
<div class="section" id="preparing-a-model-for-inference">
<h2>准备一个推理模型<a class="headerlink" href="#preparing-a-model-for-inference" title="永久链接至标题">¶</a></h2>
<div class="section" id="remove-unneeded-nodes">
<h3>删除不需要的节点<a class="headerlink" href="#remove-unneeded-nodes" title="永久链接至标题">¶</a></h3>
<p>导出过程将删除许多推理不需要的节点，但不幸的是有些不需要的节点在导出过程中无法删除。 应该手动删除的节点：</p>
<ul class="simple">
<li><p>Dropout, including <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">Dropout</a> 和 <a class="reference external" href="https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/nn/rnn_cell/DropoutWrapper?hl=hr">DropoutWrapper</a></p></li>
<li><p><a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/debugging/Assert">Assert</a></p></li>
</ul>
</div>
<div class="section" id="convert-none-dimensions-to-constants">
<h3>将维度中的 None 转换为常数<a class="headerlink" href="#convert-none-dimensions-to-constants" title="永久链接至标题">¶</a></h3>
<p>TVM 对动态张量 shape 的支持最少。维度中出现的 <code class="docutils literal notranslate"><span class="pre">None</span></code> 应替换为常数。例如，模型接受形状为 <code class="docutils literal notranslate"><span class="pre">(None,20)</span></code> 的输入。这应该转换为 <code class="docutils literal notranslate"><span class="pre">(1,20)</span></code> 的shape。应当相应地修改模型，以确保这些 shape 在整个计算图中匹配。</p>
</div>
<div class="section" id="export">
<h3>导出<a class="headerlink" href="#export" title="永久链接至标题">¶</a></h3>
<p>TensorFlow 前端需要一个冻结的 protobuf (.pb) 或保存的模型作为输入。 它目前不支持 checkpoint  (.ckpt)。 TensorFlow 前端所需的 graphdef 可以从活动会话中提取，也可以使用 <a class="reference external" href="https://github.com/apache/tvm/blob/main/python/tvm/relay/frontend/tensorflow_parser.py">TFParser</a> helper 类。</p>
<p>导出模型时应进行大量转换，以准备模型进行推理。设置 <code class="docutils literal notranslate"><span class="pre">`add_shapes=True`</span></code> 也很重要，因为这会将每个节点的输出 shape 嵌入到计算图中。 下面是一个函数，用于将模型导出为给定会话的 protobuf：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">tensorflow.tools.graph_transforms</span> <span class="kn">import</span> <span class="n">TransformGraph</span>

<span class="k">def</span> <span class="nf">export_pb</span><span class="p">(</span><span class="n">session</span><span class="p">):</span>
    <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">gfile</span><span class="o">.</span><span class="n">GFile</span><span class="p">(</span><span class="s2">&quot;myexportedmodel.pb&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;myinput1&quot;</span><span class="p">,</span> <span class="s2">&quot;myinput2&quot;</span><span class="p">]</span> <span class="c1"># replace with your input names</span>
        <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;myoutput1&quot;</span><span class="p">]</span> <span class="c1"># replace with your output names</span>
        <span class="n">graph_def</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">as_graph_def</span><span class="p">(</span><span class="n">add_shapes</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
        <span class="n">graph_def</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">convert_variables_to_constants</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">graph_def</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span>
        <span class="n">graph_def</span> <span class="o">=</span> <span class="n">TransformGraph</span><span class="p">(</span>
            <span class="n">graph_def</span><span class="p">,</span>
            <span class="n">inputs</span><span class="p">,</span>
            <span class="n">outputs</span><span class="p">,</span>
            <span class="p">[</span>
                <span class="s2">&quot;remove_nodes(op=Identity, op=CheckNumerics, op=StopGradient)&quot;</span><span class="p">,</span>
                <span class="s2">&quot;sort_by_execution_order&quot;</span><span class="p">,</span> <span class="c1"># sort by execution order after each transform to ensure correct node ordering</span>
                <span class="s2">&quot;remove_attribute(attribute_name=_XlaSeparateCompiledGradients)&quot;</span><span class="p">,</span>
                <span class="s2">&quot;remove_attribute(attribute_name=_XlaCompile)&quot;</span><span class="p">,</span>
                <span class="s2">&quot;remove_attribute(attribute_name=_XlaScope)&quot;</span><span class="p">,</span>
                <span class="s2">&quot;sort_by_execution_order&quot;</span><span class="p">,</span>
                <span class="s2">&quot;remove_device&quot;</span><span class="p">,</span>
                <span class="s2">&quot;sort_by_execution_order&quot;</span><span class="p">,</span>
                <span class="s2">&quot;fold_batch_norms&quot;</span><span class="p">,</span>
                <span class="s2">&quot;sort_by_execution_order&quot;</span><span class="p">,</span>
                <span class="s2">&quot;fold_old_batch_norms&quot;</span><span class="p">,</span>
                <span class="s2">&quot;sort_by_execution_order&quot;</span>
            <span class="p">]</span>
        <span class="p">)</span>
        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">graph_def</span><span class="o">.</span><span class="n">SerializeToString</span><span class="p">())</span>
</pre></div>
</div>
<p>另一种方法是 <a class="reference external" href="https://github.com/tensorflow/models/tree/master/research/slim#exporting-the-inference-graph">导出和冻结计算图</a></p>
</div>
</div>
<div class="section" id="import-the-model">
<h2>导入模型<a class="headerlink" href="#import-the-model" title="永久链接至标题">¶</a></h2>
<div class="section" id="explicit-shape">
<h3>明确 Shape：<a class="headerlink" href="#explicit-shape" title="永久链接至标题">¶</a></h3>
<p>为了确保在整个计算图中都可以知道 shape，请将 <code class="docutils literal notranslate"><span class="pre">`shape`</span></code> 参数传递给 <code class="docutils literal notranslate"><span class="pre">`from_tensorflow`</span></code> 。 该字典键值分别为输入名称和输入shape。 有关示例，请参阅这些  <a class="reference external" href="https://github.com/apache/tvm/blob/main/tests/python/frontend/tensorflow/test_forward.py#L36">测试用例</a> 。</p>
</div>
<div class="section" id="data-layout">
<h3>数据布局<a class="headerlink" href="#data-layout" title="永久链接至标题">¶</a></h3>
<p>大多数 TensorFlow 模型都是以 NHWC 布局发布的。 NCHW 布局通常提供更好的性能，尤其是在 GPU 上。 TensorFlow 前端可以通过将参数 <code class="docutils literal notranslate"><span class="pre">`layout='NCHW'`</span></code> 传递给 <code class="docutils literal notranslate"><span class="pre">`from_tensorflow`</span></code> 来自动转换模型的数据布局。</p>
</div>
</div>
<div class="section" id="best-practices">
<h2>最佳做法<a class="headerlink" href="#best-practices" title="永久链接至标题">¶</a></h2>
<ul class="simple">
<li><p>使用静态张量 shape 而不是动态 shape（删除 <code class="docutils literal notranslate"><span class="pre">`None`</span></code>  维度）。</p></li>
<li><p>使用静态 RNN 而不是动态 RNN，因为 <code class="docutils literal notranslate"><span class="pre">`TensorArray`</span></code> 尚未得到支持。</p></li>
</ul>
</div>
<div class="section" id="supported-ops">
<h2>支持的算子<a class="headerlink" href="#supported-ops" title="永久链接至标题">¶</a></h2>
<ul class="simple">
<li><p>Abs</p></li>
<li><p>Add</p></li>
<li><p>AddN</p></li>
<li><p>All</p></li>
<li><p>Any</p></li>
<li><p>ArgMax</p></li>
<li><p>ArgMin</p></li>
<li><p>AvgPool</p></li>
<li><p>BatchMatMul</p></li>
<li><p>BatchMatMulV2</p></li>
<li><p>BatchNormWithGlobalNormalization</p></li>
<li><p>BatchToSpaceND</p></li>
<li><p>BiasAdd</p></li>
<li><p>BroadcastTo</p></li>
<li><p>Cast</p></li>
<li><p>Ceil</p></li>
<li><p>CheckNumerics</p></li>
<li><p>ClipByValue</p></li>
<li><p>Concat</p></li>
<li><p>ConcatV2</p></li>
<li><p>Conv2D</p></li>
<li><p>Cos</p></li>
<li><p>Tan</p></li>
<li><p>CropAndResize</p></li>
<li><p>DecodeJpeg</p></li>
<li><p>DepthwiseConv2dNative</p></li>
<li><p>DepthToSpace</p></li>
<li><p>Dilation2D</p></li>
<li><p>Equal</p></li>
<li><p>Elu</p></li>
<li><p>Enter</p></li>
<li><p>Erf</p></li>
<li><p>Exit</p></li>
<li><p>Exp</p></li>
<li><p>ExpandDims</p></li>
<li><p>Fill</p></li>
<li><p>Floor</p></li>
<li><p>FloorDiv</p></li>
<li><p>FloorMod</p></li>
<li><p>FusedBatchNorm</p></li>
<li><p>FusedBatchNormV2</p></li>
<li><p>Gather</p></li>
<li><p>GatherNd</p></li>
<li><p>GatherV2</p></li>
<li><p>Greater</p></li>
<li><p>GreaterEqual</p></li>
<li><p>Identity</p></li>
<li><p>IsFinite</p></li>
<li><p>IsInf</p></li>
<li><p>IsNan</p></li>
<li><p>LeakyRelu</p></li>
<li><p>LeftShift</p></li>
<li><p>Less</p></li>
<li><p>LessEqual</p></li>
<li><p>Log</p></li>
<li><p>Log1p</p></li>
<li><p>LoopCond</p></li>
<li><p>LogicalAnd</p></li>
<li><p>LogicalOr</p></li>
<li><p>LogicalNot</p></li>
<li><p>LogSoftmax</p></li>
<li><p>LRN</p></li>
<li><p>LSTMBlockCell</p></li>
<li><p>MatMul</p></li>
<li><p>Max</p></li>
<li><p>MaxPool</p></li>
<li><p>Maximum</p></li>
<li><p>Mean</p></li>
<li><p>Merge</p></li>
<li><p>Min</p></li>
<li><p>Minimum</p></li>
<li><p>MirrorPad</p></li>
<li><p>Mod</p></li>
<li><p>Mul</p></li>
<li><p>Neg</p></li>
<li><p>NextIteration</p></li>
<li><p>NotEqual</p></li>
<li><p>OneHot</p></li>
<li><p>Pack</p></li>
<li><p>Pad</p></li>
<li><p>PadV2</p></li>
<li><p>Pow</p></li>
<li><p>Prod</p></li>
<li><p>Range</p></li>
<li><p>Rank</p></li>
<li><p>RealDiv</p></li>
<li><p>Relu</p></li>
<li><p>Relu6</p></li>
<li><p>Reshape</p></li>
<li><p>ResizeBilinear</p></li>
<li><p>ResizeBicubic</p></li>
<li><p>ResizeNearestNeighbor</p></li>
<li><p>ReverseV2</p></li>
<li><p>RightShift</p></li>
<li><p>Round</p></li>
<li><p>Rsqrt</p></li>
<li><p>Select</p></li>
<li><p>Selu</p></li>
<li><p>Shape</p></li>
<li><p>Sigmoid</p></li>
<li><p>Sign</p></li>
<li><p>Sin</p></li>
<li><p>Size</p></li>
<li><p>Slice</p></li>
<li><p>Softmax</p></li>
<li><p>Softplus</p></li>
<li><p>SpaceToBatchND</p></li>
<li><p>SpaceToDepth,</p></li>
<li><p>Split</p></li>
<li><p>SplitV</p></li>
<li><p>Sqrt</p></li>
<li><p>Square</p></li>
<li><p>SquareDifference</p></li>
<li><p>Squeeze</p></li>
<li><p>StridedSlice</p></li>
<li><p>Sub</p></li>
<li><p>Sum</p></li>
<li><p>Switch</p></li>
<li><p>Tanh</p></li>
<li><p>TensorArrayV3</p></li>
<li><p>TensorArrayScatterV3</p></li>
<li><p>TensorArrayGatherV3</p></li>
<li><p>TensorArraySizeV3</p></li>
<li><p>TensorArrayWriteV3</p></li>
<li><p>TensorArrayReadV3</p></li>
<li><p>TensorArraySplitV3</p></li>
<li><p>TensorArrayConcatV3</p></li>
<li><p>Tile</p></li>
<li><p>TopKV2</p></li>
<li><p>Transpose</p></li>
<li><p>TruncateMod</p></li>
<li><p>Unpack</p></li>
<li><p>UnravelIndex</p></li>
<li><p>Where</p></li>
<li><p>ZerosLike</p></li>
</ul>
</div>
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